Overview

Dataset statistics

Number of variables43
Number of observations1573
Missing cells3849
Missing cells (%)5.7%
Duplicate rows34
Duplicate rows (%)2.2%
Total size in memory528.6 KiB
Average record size in memory344.1 B

Variable types

CAT17
NUM16
BOOL7
URL3

Warnings

currency_buyer has constant value "1573" Constant
theme has constant value "1573" Constant
crawl_month has constant value "1573" Constant
Dataset has 34 (2.2%) duplicate rows Duplicates
title has a high cardinality: 1201 distinct values High cardinality
title_orig has a high cardinality: 1203 distinct values High cardinality
tags has a high cardinality: 1230 distinct values High cardinality
product_color has a high cardinality: 101 distinct values High cardinality
product_variation_size_id has a high cardinality: 106 distinct values High cardinality
merchant_title has a high cardinality: 958 distinct values High cardinality
merchant_name has a high cardinality: 957 distinct values High cardinality
merchant_info_subtitle has a high cardinality: 1058 distinct values High cardinality
merchant_id has a high cardinality: 958 distinct values High cardinality
product_id has a high cardinality: 1341 distinct values High cardinality
rating_five_count is highly correlated with rating_count and 2 other fieldsHigh correlation
rating_count is highly correlated with rating_five_count and 4 other fieldsHigh correlation
rating_four_count is highly correlated with rating_count and 3 other fieldsHigh correlation
rating_three_count is highly correlated with rating_count and 4 other fieldsHigh correlation
rating_two_count is highly correlated with rating_count and 3 other fieldsHigh correlation
rating_one_count is highly correlated with rating_count and 2 other fieldsHigh correlation
rating_five_count has 45 (2.9%) missing values Missing
rating_four_count has 45 (2.9%) missing values Missing
rating_three_count has 45 (2.9%) missing values Missing
rating_two_count has 45 (2.9%) missing values Missing
rating_one_count has 45 (2.9%) missing values Missing
product_color has 41 (2.6%) missing values Missing
has_urgency_banner has 1100 (69.9%) missing values Missing
urgency_text has 1100 (69.9%) missing values Missing
origin_country has 17 (1.1%) missing values Missing
merchant_profile_picture has 1347 (85.6%) missing values Missing
tags is uniformly distributed Uniform
merchant_info_subtitle is uniformly distributed Uniform
product_id is uniformly distributed Uniform
rating_count has 45 (2.9%) zeros Zeros
rating_five_count has 31 (2.0%) zeros Zeros
rating_four_count has 96 (6.1%) zeros Zeros
rating_three_count has 138 (8.8%) zeros Zeros
rating_two_count has 196 (12.5%) zeros Zeros
rating_one_count has 116 (7.4%) zeros Zeros

Reproduction

Analysis started2020-09-11 02:58:25.134635
Analysis finished2020-09-11 02:59:03.962997
Duration38.83 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

title
Categorical

HIGH CARDINALITY

Distinct1201
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage
 
24
Mini robe de soirée décontractée sans manches pour femmes
 
12
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper
 
9
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté
 
9
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress
 
9
Other values (1196)
1510 
ValueCountFrequency (%) 
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage241.5%
 
Mini robe de soirée décontractée sans manches pour femmes120.8%
 
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper90.6%
 
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté90.6%
 
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress90.6%
 
Mode féminine été bretelles spaghetti imprimé floral nouer devant mini robe robe sexy80.5%
 
Costume de sport cool pour hommes d'été Vêtements de sport Costumes de jogging décontractés Ensembles de tenues à manches courtes70.4%
 
Femmes été décontracté lâche couleur unie salopette vintage sangle pantalon long combinaisons barboteuses grande taille70.4%
 
Pantalon de mode d'été Femmes Leggings Pantalon déchiré Pantalon slim Pantalon vert armée Collants70.4%
 
Mode d'été femme papillon réservoir gilet sans manches col rond haut décontracté60.4%
 
Other values (1191)147593.8%
 
2020-09-10T22:59:04.087666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique988 ?
Unique (%)62.8%
2020-09-10T22:59:04.244247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length327
Median length112
Mean length116.9211697
Min length27

title_orig
Categorical

HIGH CARDINALITY

Distinct1203
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress
 
24
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress
 
12
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper
 
9
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress
 
9
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants
 
9
Other values (1198)
1510 
ValueCountFrequency (%) 
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress241.5%
 
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress120.8%
 
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper90.6%
 
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress90.6%
 
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants90.6%
 
Women's Summer Fashion Spaghetti Strap Floral Print Tie Front Mini Dress Sexy Dress80.5%
 
Summer mens cool sport suit Sports Wear Casual Jogging Suits Short Sleeve Outfit Sets70.4%
 
Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants70.4%
 
Women Summer Casual Loose Solid Color Vintage Overalls Strap Long Pants Jumpsuits Rompers Plus Size70.4%
 
Women Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL60.4%
 
Other values (1193)147593.8%
 
2020-09-10T22:59:04.409804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique990 ?
Unique (%)62.9%
2020-09-10T22:59:04.558442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length272
Median length96
Mean length102.6280992
Min length21

price
Real number (ℝ≥0)

Distinct127
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.325371901
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:04.702057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.656
Q15.81
median8
Q311
95-th percentile15
Maximum49
Range48
Interquartile range (IQR)5.19

Descriptive statistics

Standard deviation3.932029815
Coefficient of variation (CV)0.472294795
Kurtosis7.765125164
Mean8.325371901
Median Absolute Deviation (MAD)3
Skewness1.3158913
Sum13095.81
Variance15.46085847
MonotocityNot monotonic
2020-09-10T22:59:04.831710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
828217.9%
 
1120212.8%
 
71298.2%
 
91268.0%
 
61207.6%
 
12815.1%
 
5805.1%
 
14573.6%
 
13543.4%
 
16422.7%
 
Other values (117)40025.4%
 
ValueCountFrequency (%) 
150.3%
 
1.6520.1%
 
1.6620.1%
 
1.6710.1%
 
1.6850.3%
 
ValueCountFrequency (%) 
4910.1%
 
2710.1%
 
2610.1%
 
2510.1%
 
2410.1%
 

retail_price
Real number (ℝ≥0)

Distinct104
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.28862047
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:04.966349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median10
Q326
95-th percentile85
Maximum252
Range251
Interquartile range (IQR)19

Descriptive statistics

Standard deviation30.35786309
Coefficient of variation (CV)1.303549222
Kurtosis10.04536878
Mean23.28862047
Median Absolute Deviation (MAD)5
Skewness2.742709302
Sum36633
Variance921.5998513
MonotocityNot monotonic
2020-09-10T22:59:05.096004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
717711.3%
 
61368.6%
 
101288.1%
 
51026.5%
 
11996.3%
 
8905.7%
 
9543.4%
 
4503.2%
 
17473.0%
 
2452.9%
 
Other values (94)64541.0%
 
ValueCountFrequency (%) 
110.1%
 
2452.9%
 
3372.4%
 
4503.2%
 
51026.5%
 
ValueCountFrequency (%) 
25220.1%
 
25010.1%
 
16910.1%
 
16850.3%
 
15940.3%
 

currency_buyer
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
EUR
1573 
ValueCountFrequency (%) 
EUR1573100.0%
 
2020-09-10T22:59:05.209663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-10T22:59:05.277483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:05.358266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

units_sold
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4339.005086
Minimum1
Maximum100000
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:05.462025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1100
median1000
Q35000
95-th percentile20000
Maximum100000
Range99999
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation9356.539302
Coefficient of variation (CV)2.156378966
Kurtosis45.56805559
Mean4339.005086
Median Absolute Deviation (MAD)900
Skewness5.624840138
Sum6825255
Variance87544827.71
MonotocityNot monotonic
2020-09-10T22:59:05.860922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
10050932.4%
 
100040525.7%
 
500021713.8%
 
1000017711.3%
 
200001036.5%
 
50764.8%
 
10493.1%
 
50000171.1%
 
10000060.4%
 
840.3%
 
Other values (5)100.6%
 
ValueCountFrequency (%) 
130.2%
 
220.1%
 
320.1%
 
610.1%
 
720.1%
 
ValueCountFrequency (%) 
10000060.4%
 
50000171.1%
 
200001036.5%
 
1000017711.3%
 
500021713.8%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
892 
1
681 
ValueCountFrequency (%) 
089256.7%
 
168143.3%
 
2020-09-10T22:59:05.946693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

rating
Real number (ℝ≥0)

Distinct192
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.820896376
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:06.042434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.55
median3.85
Q34.11
95-th percentile4.698
Maximum5
Range4
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.5153735991
Coefficient of variation (CV)0.134882904
Kurtosis2.735180424
Mean3.820896376
Median Absolute Deviation (MAD)0.28
Skewness-0.5309121947
Sum6010.27
Variance0.2656099466
MonotocityNot monotonic
2020-09-10T22:59:06.195340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5704.5%
 
4442.8%
 
3.67332.1%
 
3221.4%
 
4.07221.4%
 
3.61211.3%
 
3.8211.3%
 
3.96201.3%
 
3.7191.2%
 
3.75191.2%
 
Other values (182)128281.5%
 
ValueCountFrequency (%) 
130.2%
 
1.520.1%
 
290.6%
 
2.2510.1%
 
2.3320.1%
 
ValueCountFrequency (%) 
5704.5%
 
4.8610.1%
 
4.8310.1%
 
4.820.1%
 
4.7540.3%
 

rating_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct761
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean889.6592498
Minimum0
Maximum20744
Zeros45
Zeros (%)2.9%
Memory size12.3 KiB
2020-09-10T22:59:06.356266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median150
Q3855
95-th percentile3771.4
Maximum20744
Range20744
Interquartile range (IQR)831

Descriptive statistics

Standard deviation1983.928834
Coefficient of variation (CV)2.229987306
Kurtosis30.00764131
Mean889.6592498
Median Absolute Deviation (MAD)146
Skewness4.789467043
Sum1399434
Variance3935973.619
MonotocityNot monotonic
2020-09-10T22:59:06.488705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0452.9%
 
2312.0%
 
4281.8%
 
6261.7%
 
12251.6%
 
3231.5%
 
10201.3%
 
8191.2%
 
1191.2%
 
13171.1%
 
Other values (751)132083.9%
 
ValueCountFrequency (%) 
0452.9%
 
1191.2%
 
2312.0%
 
3231.5%
 
4281.8%
 
ValueCountFrequency (%) 
2074410.1%
 
1846310.1%
 
1839310.1%
 
1798010.1%
 
1744410.1%
 

rating_five_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct605
Distinct (%)39.6%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean442.2637435
Minimum0
Maximum11548
Zeros31
Zeros (%)2.0%
Memory size12.3 KiB
2020-09-10T22:59:06.630905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median79
Q3413.5
95-th percentile2048.25
Maximum11548
Range11548
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation980.2032696
Coefficient of variation (CV)2.21633196
Kurtosis34.12450729
Mean442.2637435
Median Absolute Deviation (MAD)76
Skewness4.930986369
Sum675779
Variance960798.4497
MonotocityNot monotonic
2020-09-10T22:59:06.772369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5493.1%
 
1493.1%
 
3442.8%
 
2432.7%
 
4342.2%
 
0312.0%
 
9261.7%
 
7241.5%
 
8211.3%
 
17211.3%
 
Other values (595)118675.4%
 
(Missing)452.9%
 
ValueCountFrequency (%) 
0312.0%
 
1493.1%
 
2432.7%
 
3442.8%
 
4342.2%
 
ValueCountFrequency (%) 
1154810.1%
 
1118410.1%
 
829010.1%
 
753010.1%
 
733710.1%
 

rating_four_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct440
Distinct (%)28.8%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean179.5994764
Minimum0
Maximum4152
Zeros96
Zeros (%)6.1%
Memory size12.3 KiB
2020-09-10T22:59:06.913876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31.5
Q3168.25
95-th percentile758.6
Maximum4152
Range4152
Interquartile range (IQR)163.25

Descriptive statistics

Standard deviation400.5162311
Coefficient of variation (CV)2.230052331
Kurtosis27.71305173
Mean179.5994764
Median Absolute Deviation (MAD)30.5
Skewness4.665102585
Sum274428
Variance160413.2514
MonotocityNot monotonic
2020-09-10T22:59:07.038454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0966.1%
 
1915.8%
 
2674.3%
 
4533.4%
 
3533.4%
 
5523.3%
 
7322.0%
 
11281.8%
 
6271.7%
 
8271.7%
 
Other values (430)100263.7%
 
(Missing)452.9%
 
ValueCountFrequency (%) 
0966.1%
 
1915.8%
 
2674.3%
 
3533.4%
 
4533.4%
 
ValueCountFrequency (%) 
415210.1%
 
348310.1%
 
340410.1%
 
335110.1%
 
319110.1%
 

rating_three_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct384
Distinct (%)25.1%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean134.5497382
Minimum0
Maximum3658
Zeros138
Zeros (%)8.8%
Memory size12.3 KiB
2020-09-10T22:59:07.166816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median24
Q3129.25
95-th percentile574.6
Maximum3658
Range3658
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation311.6906559
Coefficient of variation (CV)2.316545985
Kurtosis35.54985044
Mean134.5497382
Median Absolute Deviation (MAD)24
Skewness5.174655967
Sum205592
Variance97151.06498
MonotocityNot monotonic
2020-09-10T22:59:07.294474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01388.8%
 
1865.5%
 
2795.0%
 
5533.4%
 
3513.2%
 
4503.2%
 
6362.3%
 
7342.2%
 
10312.0%
 
8251.6%
 
Other values (374)94560.1%
 
(Missing)452.9%
 
ValueCountFrequency (%) 
01388.8%
 
1865.5%
 
2795.0%
 
3513.2%
 
4503.2%
 
ValueCountFrequency (%) 
365810.1%
 
305710.1%
 
295110.1%
 
291910.1%
 
262410.1%
 

rating_two_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct262
Distinct (%)17.1%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean63.71138743
Minimum0
Maximum2003
Zeros196
Zeros (%)12.5%
Memory size12.3 KiB
2020-09-10T22:59:07.434100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q362
95-th percentile275.55
Maximum2003
Range2003
Interquartile range (IQR)60

Descriptive statistics

Standard deviation151.343933
Coefficient of variation (CV)2.375461266
Kurtosis44.81767579
Mean63.71138743
Median Absolute Deviation (MAD)11
Skewness5.665096423
Sum97351
Variance22904.98607
MonotocityNot monotonic
2020-09-10T22:59:07.565745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
019612.5%
 
115910.1%
 
2845.3%
 
3704.5%
 
4563.6%
 
5402.5%
 
6382.4%
 
7362.3%
 
8301.9%
 
11231.5%
 
Other values (252)79650.6%
 
(Missing)452.9%
 
ValueCountFrequency (%) 
019612.5%
 
115910.1%
 
2845.3%
 
3704.5%
 
4563.6%
 
ValueCountFrequency (%) 
200310.1%
 
173610.1%
 
141010.1%
 
131010.1%
 
117410.1%
 

rating_one_count
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct330
Distinct (%)21.6%
Missing45
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean95.73560209
Minimum0
Maximum2789
Zeros116
Zeros (%)7.4%
Memory size12.3 KiB
2020-09-10T22:59:07.724329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median20
Q394
95-th percentile396.65
Maximum2789
Range2789
Interquartile range (IQR)90

Descriptive statistics

Standard deviation214.0755444
Coefficient of variation (CV)2.236112164
Kurtosis41.82846709
Mean95.73560209
Median Absolute Deviation (MAD)19
Skewness5.383055991
Sum146284
Variance45828.33869
MonotocityNot monotonic
2020-09-10T22:59:07.844999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01167.4%
 
11167.4%
 
3764.8%
 
2734.6%
 
4593.8%
 
7462.9%
 
5392.5%
 
6322.0%
 
8322.0%
 
9271.7%
 
Other values (320)91258.0%
 
(Missing)452.9%
 
ValueCountFrequency (%) 
01167.4%
 
11167.4%
 
2734.6%
 
3764.8%
 
4593.8%
 
ValueCountFrequency (%) 
278910.1%
 
255910.1%
 
184610.1%
 
173610.1%
 
160010.1%
 

badges_count
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1422 
1
 
138
2
 
11
3
 
2
ValueCountFrequency (%) 
0142290.4%
 
11388.8%
 
2110.7%
 
320.1%
 
2020-09-10T22:59:07.970661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-10T22:59:08.042470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:08.126247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1544 
1
 
29
ValueCountFrequency (%) 
0154498.2%
 
1291.8%
 
2020-09-10T22:59:08.193067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1456 
1
 
117
ValueCountFrequency (%) 
0145692.6%
 
11177.4%
 
2020-09-10T22:59:08.230966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1553 
1
 
20
ValueCountFrequency (%) 
0155398.7%
 
1201.3%
 
2020-09-10T22:59:08.269863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

tags
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1230
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women
 
17
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress
 
9
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt
 
8
Summer,short sleeve dress,neck dress,Necks,Sleeve,Beach,Dress,Loose,short sleeves,V-neck,Shorts,beach dress,Plus Size,Midi Dress,summer dress,Print,Pullovers,Women's Fashion,Casual,Women
 
7
Summer,Fashion,Necks,Beach,Dress,Loose,beach dress,Round neck,Women's Fashion,sleeveless,Skirts,Casual,Women
 
7
Other values (1225)
1525 
ValueCountFrequency (%) 
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women171.1%
 
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress90.6%
 
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt80.5%
 
Summer,short sleeve dress,neck dress,Necks,Sleeve,Beach,Dress,Loose,short sleeves,V-neck,Shorts,beach dress,Plus Size,Midi Dress,summer dress,Print,Pullovers,Women's Fashion,Casual,Women70.4%
 
Summer,Fashion,Necks,Beach,Dress,Loose,beach dress,Round neck,Women's Fashion,sleeveless,Skirts,Casual,Women70.4%
 
Summer,Women Rompers,Plus Size,women long pants,linenjumpsuit,pants,Overalls,Loose,plussizejumpsuit,Women's Fashion,strappant,Long pants,Jumpsuits & Rompers,rompers womens jumpsuit,Vintage,Women,women Jumpsuit,Casual,jumpsuit70.4%
 
Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,Women60.4%
 
Mini,womens dresses,Summer,sleevele,Dress,Mini dress,Women's Fashion,Fashion,backless,party,sexy,summer dresses,Women S Clothing,Casual,sleeveless50.3%
 
pajamaset,Fashion,sexy pajamas for womens,silksleepwearforwomen,silksleepwear,pajamassuit,Casual,Women's Fashion,Summer,Sleepwear,pajamasforwomen,silksleepwearnightgown,silk,pajamassleepwear,women's pajamas,Women50.3%
 
butterfly,Mini,Vest,Fashion,Tank,Necks,Summer,womemstop,Tops,tank top,Round neck,#top #crop,tankvest,sleeveless,Casual50.3%
 
Other values (1220)149795.2%
 
2020-09-10T22:59:08.371588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1005 ?
Unique (%)63.9%
2020-09-10T22:59:08.531126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length448
Median length165
Mean length169.1411316
Min length61

product_color
Categorical

HIGH CARDINALITY
MISSING

Distinct101
Distinct (%)6.6%
Missing41
Missing (%)2.6%
Memory size12.3 KiB
black
302 
white
254 
yellow
105 
blue
99 
pink
99 
Other values (96)
673 
ValueCountFrequency (%) 
black30219.2%
 
white25416.1%
 
yellow1056.7%
 
blue996.3%
 
pink996.3%
 
red935.9%
 
green905.7%
 
grey714.5%
 
purple533.4%
 
armygreen312.0%
 
Other values (91)33521.3%
 
(Missing)412.6%
 
2020-09-10T22:59:08.690744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique39 ?
Unique (%)2.5%
2020-09-10T22:59:08.826373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length5
Mean length5.466624285
Min length3

product_variation_size_id
Categorical

HIGH CARDINALITY

Distinct106
Distinct (%)6.8%
Missing14
Missing (%)0.9%
Memory size12.3 KiB
S
641 
XS
356 
M
200 
XXS
100 
L
 
49
Other values (101)
213 
ValueCountFrequency (%) 
S64140.8%
 
XS35622.6%
 
M20012.7%
 
XXS1006.4%
 
L493.1%
 
S.181.1%
 
XL171.1%
 
XXL151.0%
 
XXXS60.4%
 
4XL50.3%
 
Other values (96)1529.7%
 
(Missing)140.9%
 
2020-09-10T22:59:08.961014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique63 ?
Unique (%)4.0%
2020-09-10T22:59:09.090665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length1
Mean length1.944055944
Min length1

product_variation_inventory
Real number (ℝ≥0)

Distinct48
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.08137317
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:09.212339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)44

Descriptive statistics

Standard deviation21.35313744
Coefficient of variation (CV)0.6454731286
Kurtosis-1.574971382
Mean33.08137317
Median Absolute Deviation (MAD)0
Skewness-0.5656531028
Sum52037
Variance455.9564785
MonotocityNot monotonic
2020-09-10T22:59:09.337006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
5090757.7%
 
11529.7%
 
2815.1%
 
5744.7%
 
3523.3%
 
10402.5%
 
4251.6%
 
9221.4%
 
7181.1%
 
6181.1%
 
Other values (38)18411.7%
 
ValueCountFrequency (%) 
11529.7%
 
2815.1%
 
3523.3%
 
4251.6%
 
5744.7%
 
ValueCountFrequency (%) 
5090757.7%
 
4990.6%
 
4840.3%
 
4740.3%
 
4660.4%
 
Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
Livraison standard
1508 
Standard Shipping
 
21
Envio Padrão
 
9
Expediere Standard
 
6
Envío normal
 
5
Other values (10)
 
24
ValueCountFrequency (%) 
Livraison standard150895.9%
 
Standard Shipping211.3%
 
Envio Padrão90.6%
 
Expediere Standard60.4%
 
Envío normal50.3%
 
الشحن القياسي40.3%
 
Standardversand30.2%
 
Стандартная доставка30.2%
 
Livraison Express30.2%
 
Standardowa wysyłka30.2%
 
Other values (5)80.5%
 
2020-09-10T22:59:09.477595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-09-10T22:59:09.605253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length18
Mean length17.92180547
Min length12

shipping_option_price
Real number (ℝ≥0)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.345200254
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:09.709973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.024371048
Coefficient of variation (CV)0.4367947029
Kurtosis6.534053273
Mean2.345200254
Median Absolute Deviation (MAD)1
Skewness1.365252463
Sum3689
Variance1.049336044
MonotocityNot monotonic
2020-09-10T22:59:09.803759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
261939.4%
 
352033.1%
 
130819.6%
 
4764.8%
 
5322.0%
 
6120.8%
 
750.3%
 
1210.1%
 
ValueCountFrequency (%) 
130819.6%
 
261939.4%
 
352033.1%
 
4764.8%
 
5322.0%
 
ValueCountFrequency (%) 
1210.1%
 
750.3%
 
6120.8%
 
5322.0%
 
4764.8%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1569 
1
 
4
ValueCountFrequency (%) 
0156999.7%
 
140.3%
 
2020-09-10T22:59:09.867589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

countries_shipped_to
Real number (ℝ≥0)

Distinct94
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.45645264
Minimum6
Maximum140
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:09.956350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18
Q131
median40
Q343
95-th percentile72.4
Maximum140
Range134
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.30120308
Coefficient of variation (CV)0.501803835
Kurtosis11.38391925
Mean40.45645264
Median Absolute Deviation (MAD)5
Skewness2.961890329
Sum63638
Variance412.1388467
MonotocityNot monotonic
2020-09-10T22:59:10.091994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4117110.9%
 
4317010.8%
 
401066.7%
 
38764.8%
 
36644.1%
 
35613.9%
 
42573.6%
 
39432.7%
 
25392.5%
 
37382.4%
 
Other values (84)74847.6%
 
ValueCountFrequency (%) 
610.1%
 
860.4%
 
940.3%
 
1070.4%
 
1120.1%
 
ValueCountFrequency (%) 
14030.2%
 
139140.9%
 
13890.6%
 
13730.2%
 
13520.1%
 

inventory_total
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.82136046
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:10.211667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q150
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.562799326
Coefficient of variation (CV)0.05143977007
Kurtosis287.2634299
Mean49.82136046
Median Absolute Deviation (MAD)0
Skewness-16.47787667
Sum78369
Variance6.567940387
MonotocityNot monotonic
2020-09-10T22:59:10.306415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50156399.4%
 
220.1%
 
4010.1%
 
3810.1%
 
3710.1%
 
3610.1%
 
3010.1%
 
2410.1%
 
910.1%
 
110.1%
 
ValueCountFrequency (%) 
110.1%
 
220.1%
 
910.1%
 
2410.1%
 
3010.1%
 
ValueCountFrequency (%) 
50156399.4%
 
4010.1%
 
3810.1%
 
3710.1%
 
3610.1%
 

has_urgency_banner
Boolean

MISSING

Distinct1
Distinct (%)0.2%
Missing1100
Missing (%)69.9%
Memory size12.3 KiB
1
473 
(Missing)
1100 
ValueCountFrequency (%) 
147330.1%
 
(Missing)110069.9%
 
2020-09-10T22:59:10.375224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

urgency_text
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing1100
Missing (%)69.9%
Memory size12.3 KiB
Quantité limitée !
472 
Réduction sur les achats en gros
 
1
ValueCountFrequency (%) 
Quantité limitée !47230.0%
 
Réduction sur les achats en gros10.1%
 
(Missing)110069.9%
 
2020-09-10T22:59:10.437065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-09-10T22:59:10.496904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:10.574658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length3
Mean length7.519389701
Min length3

origin_country
Categorical

MISSING

Distinct6
Distinct (%)0.4%
Missing17
Missing (%)1.1%
Memory size12.3 KiB
CN
1516 
US
 
31
VE
 
5
SG
 
2
AT
 
1
ValueCountFrequency (%) 
CN151696.4%
 
US312.0%
 
VE50.3%
 
SG20.1%
 
AT10.1%
 
GB10.1%
 
(Missing)171.1%
 
2020-09-10T22:59:10.675426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-09-10T22:59:10.748229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:10.847964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.010807374
Min length2

merchant_title
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
guangzhouweishiweifushiyouxiangongsi
 
15
Suyi Technology
 
12
sjhdstoer
 
9
shuilingjiao international trade company
 
8
Cenic Beauty
 
8
Other values (953)
1521 
ValueCountFrequency (%) 
guangzhouweishiweifushiyouxiangongsi151.0%
 
Suyi Technology120.8%
 
sjhdstoer90.6%
 
shuilingjiao international trade company80.5%
 
Cenic Beauty80.5%
 
Sangboo Store80.5%
 
Pentiumhorse70.4%
 
fengjinying60.4%
 
sklioppp60.4%
 
Smart Home International Co.Ltd60.4%
 
Other values (948)148894.6%
 
2020-09-10T22:59:10.980609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique635 ?
Unique (%)40.4%
2020-09-10T22:59:11.123230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length51
Median length11
Mean length12.26636999
Min length2

merchant_name
Categorical

HIGH CARDINALITY

Distinct957
Distinct (%)61.0%
Missing4
Missing (%)0.3%
Memory size12.3 KiB
广州唯适唯服饰有限公司
 
15
greatexpectationstechnology
 
12
sjhdstoer
 
9
sangboostore
 
8
shuilingjiaointernationaltradecompany
 
8
Other values (952)
1517 
ValueCountFrequency (%) 
广州唯适唯服饰有限公司151.0%
 
greatexpectationstechnology120.8%
 
sjhdstoer90.6%
 
sangboostore80.5%
 
shuilingjiaointernationaltradecompany80.5%
 
cenicbeauty80.5%
 
pentiumhorse70.4%
 
fengjinying60.4%
 
zuilangmands60.4%
 
sklioppp60.4%
 
Other values (947)148494.3%
 
2020-09-10T22:59:11.268838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique635 ?
Unique (%)40.5%
2020-09-10T22:59:11.426382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length11
Mean length11.71964399
Min length2

merchant_info_subtitle
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1058
Distinct (%)67.3%
Missing1
Missing (%)0.1%
Memory size12.3 KiB
83 % avis positifs (32,168 notes)
 
14
86 % avis positifs (12,309 notes)
 
11
87 % avis positifs (42,919 notes)
 
8
85 % avis positifs (80,093 notes)
 
7
84 % avis positifs (5,654 notes)
 
6
Other values (1053)
1526 
ValueCountFrequency (%) 
83 % avis positifs (32,168 notes)140.9%
 
86 % avis positifs (12,309 notes)110.7%
 
87 % avis positifs (42,919 notes)80.5%
 
85 % avis positifs (80,093 notes)70.4%
 
84 % avis positifs (5,654 notes)60.4%
 
84 % avis positifs (36,361 notes)60.4%
 
86 % avis positifs (65,189 notes)60.4%
 
89 % avis positifs (55,499 notes)60.4%
 
84 % avis positifs (1,047 notes)50.3%
 
85 % avis positifs (5,264 notes)50.3%
 
Other values (1048)149895.2%
 
2020-09-10T22:59:11.575021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique742 ?
Unique (%)47.2%
2020-09-10T22:59:11.706667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length54
Median length32
Mean length28.79148125
Min length3

merchant_rating_count
Real number (ℝ≥0)

Distinct917
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26495.8328
Minimum0
Maximum2174765
Zeros1
Zeros (%)0.1%
Memory size12.3 KiB
2020-09-10T22:59:11.828342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.4
Q11987
median7936
Q324564
95-th percentile105015
Maximum2174765
Range2174765
Interquartile range (IQR)22577

Descriptive statistics

Standard deviation78474.45561
Coefficient of variation (CV)2.961765957
Kurtosis380.1505712
Mean26495.8328
Median Absolute Deviation (MAD)7334
Skewness15.88901874
Sum41677945
Variance6158240183
MonotocityNot monotonic
2020-09-10T22:59:11.960993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32168151.0%
 
12309120.8%
 
8009390.6%
 
4291980.5%
 
8819380.5%
 
1060080.5%
 
5549970.4%
 
3170.4%
 
12960.4%
 
6518960.4%
 
Other values (907)148794.5%
 
ValueCountFrequency (%) 
010.1%
 
320.1%
 
420.1%
 
640.3%
 
810.1%
 
ValueCountFrequency (%) 
217476510.1%
 
83988230.2%
 
40274330.2%
 
36689810.1%
 
33040510.1%
 

merchant_rating
Real number (ℝ≥0)

Distinct952
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.032345119
Minimum2.333333333
Maximum5
Zeros0
Zeros (%)0.0%
Memory size12.3 KiB
2020-09-10T22:59:12.097622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.333333333
5-th percentile3.70758405
Q13.917353419
median4.040650407
Q34.161796723
95-th percentile4.325714254
Maximum5
Range2.666666667
Interquartile range (IQR)0.2444433038

Descriptive statistics

Standard deviation0.204767997
Coefficient of variation (CV)0.0507813669
Kurtosis5.316849955
Mean4.032345119
Median Absolute Deviation (MAD)0.1222830596
Skewness-1.029755428
Sum6342.878873
Variance0.04192993259
MonotocityNot monotonic
2020-09-10T22:59:12.646154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.884543646151.0%
 
4.045170201120.8%
 
4.0066922290.6%
 
4.08089077480.5%
 
3.8675471780.5%
 
4.10596705480.5%
 
4.13888538570.4%
 
4.14695734760.4%
 
3.89967327660.4%
 
4.04964027760.4%
 
Other values (942)148894.6%
 
ValueCountFrequency (%) 
2.33333333310.1%
 
2.94117647110.1%
 
310.1%
 
3.03448275910.1%
 
3.03896103910.1%
 
ValueCountFrequency (%) 
510.1%
 
4.5775193810.1%
 
4.52186588910.1%
 
4.512510.1%
 
4.50147203110.1%
 

merchant_id
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
558c2cdc89d53c4005ea2920
 
15
5acaf29d5ebcfd72403106a8
 
12
583138b06339b410ab9663ec
 
9
582833faea77701b456c786a
 
8
564d8a9ac0f55a1276cd96f8
 
8
Other values (953)
1521 
ValueCountFrequency (%) 
558c2cdc89d53c4005ea2920151.0%
 
5acaf29d5ebcfd72403106a8120.8%
 
583138b06339b410ab9663ec90.6%
 
582833faea77701b456c786a80.5%
 
564d8a9ac0f55a1276cd96f880.5%
 
5533c83986ff95173dc017d080.5%
 
5926c5ace8ff5525241b368d70.4%
 
5b160017daac45594728d9ba60.4%
 
5639cd9bddb94f103070ef9f60.4%
 
56fba259138ef73c2a749a5660.4%
 
Other values (948)148894.6%
 
2020-09-10T22:59:12.796750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique635 ?
Unique (%)40.4%
2020-09-10T22:59:12.919423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length24
Mean length24
Min length24
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0
1347 
1
226 
ValueCountFrequency (%) 
0134785.6%
 
122614.4%
 
2020-09-10T22:59:12.986243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct125
Distinct (%)55.3%
Missing1347
Missing (%)85.6%
Memory size12.3 KiB
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg
 
12
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg
 
8
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg
 
6
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg
 
6
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_577fb2b368116418674befd9.jpg
 
5
Other values (120)
189 
(Missing)
1347 
ValueCountFrequency (%) 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg120.8%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg80.5%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg60.4%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg60.4%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_577fb2b368116418674befd9.jpg50.3%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5268759b736046671957190c.jpg50.3%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_539937d634067e06707b1a8e.jpg40.3%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_557ed5b886d66519ff242099.jpg40.3%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_58131ddf6f55296033923a7c.jpg40.3%
 
https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_55ed5a3362e273427107759e.jpg40.3%
 
Other values (115)16810.7%
 
(Missing)134785.6%
 
ValueCountFrequency (%) 
https22614.4%
 
(Missing)134785.6%
 
ValueCountFrequency (%) 
s3-us-west-1.amazonaws.com22614.4%
 
(Missing)134785.6%
 
ValueCountFrequency (%) 
/sweeper-production-merchantimage/dp_5acaf29d5ebcfd72403106a8.jpg120.8%
 
/sweeper-production-merchantimage/dp_5533c83986ff95173dc017d0.jpg80.5%
 
/sweeper-production-merchantimage/dp_55c8a4c33a698c6010edcd9e.jpg60.4%
 
/sweeper-production-merchantimage/dp_58ad449708de0c6dc59d9e06.jpg60.4%
 
/sweeper-production-merchantimage/dp_5268759b736046671957190c.jpg50.3%
 
/sweeper-production-merchantimage/dp_577fb2b368116418674befd9.jpg50.3%
 
/sweeper-production-merchantimage/dp_5495976a40b3782bc8b3654a.jpg40.3%
 
/sweeper-production-merchantimage/dp_57c306d73a698c06f98ed450.jpg40.3%
 
/sweeper-production-merchantimage/dp_557ed5b886d66519ff242099.jpg40.3%
 
/sweeper-production-merchantimage/dp_58131ddf6f55296033923a7c.jpg40.3%
 
Other values (115)16810.7%
 
(Missing)134785.6%
 
ValueCountFrequency (%) 
22614.4%
 
(Missing)134785.6%
 
ValueCountFrequency (%) 
22614.4%
 
(Missing)134785.6%
 
Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
https://www.wish.com/c/5cde56ea6bbbd86b1cbab4a8
 
3
https://www.wish.com/c/5e9a74e447f7d92c8db8d14b
 
3
https://www.wish.com/c/5dea1d9cec016f062ce8aab1
 
3
https://www.wish.com/c/5ebff6d34a4cf4438dba5d80
 
3
https://www.wish.com/c/5e142dee04c3e579e89576a3
 
3
Other values (1336)
1558 
ValueCountFrequency (%) 
https://www.wish.com/c/5cde56ea6bbbd86b1cbab4a830.2%
 
https://www.wish.com/c/5e9a74e447f7d92c8db8d14b30.2%
 
https://www.wish.com/c/5dea1d9cec016f062ce8aab130.2%
 
https://www.wish.com/c/5ebff6d34a4cf4438dba5d8030.2%
 
https://www.wish.com/c/5e142dee04c3e579e89576a330.2%
 
https://www.wish.com/c/5e16cb87e6dd7c03be24b28a30.2%
 
https://www.wish.com/c/5e93d60ebc5446aedde50c5030.2%
 
https://www.wish.com/c/5eba05b08c884a0bddd0ad9630.2%
 
https://www.wish.com/c/5eb4dd169263020a42be1a8830.2%
 
https://www.wish.com/c/5ec1e63f7abee20ab93c68f230.2%
 
Other values (1331)154398.1%
 
ValueCountFrequency (%) 
https1573100.0%
 
ValueCountFrequency (%) 
www.wish.com1573100.0%
 
ValueCountFrequency (%) 
/c/5e16cb87e6dd7c03be24b28a30.2%
 
/c/5c80e8a150c63d28c67b8f1430.2%
 
/c/5ebe0ead593b960eb1c82d0b30.2%
 
/c/5eb2200b989caa081980b81230.2%
 
/c/5cde56ea6bbbd86b1cbab4a830.2%
 
/c/5eba5b1c29367c77b5c0eb3530.2%
 
/c/5ec1e63f7abee20ab93c68f230.2%
 
/c/5e93d60ebc5446aedde50c5030.2%
 
/c/5e142dee04c3e579e89576a330.2%
 
/c/5ee8875404718a4bba2d634830.2%
 
Other values (1331)154398.1%
 
ValueCountFrequency (%) 
1573100.0%
 
ValueCountFrequency (%) 
1573100.0%
 
Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
https://contestimg.wish.com/api/webimage/5e9932cab3eafb25c00ba79f-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5c80e8a150c63d28c67b8f14-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5cde56ea6bbbd86b1cbab4a8-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5e9dad8cbc19c300417e1733-medium.jpg
 
3
https://contestimg.wish.com/api/webimage/5e93d60ebc5446aedde50c50-medium.jpg
 
3
Other values (1336)
1558 
ValueCountFrequency (%) 
https://contestimg.wish.com/api/webimage/5e9932cab3eafb25c00ba79f-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5c80e8a150c63d28c67b8f14-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5cde56ea6bbbd86b1cbab4a8-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5e9dad8cbc19c300417e1733-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5e93d60ebc5446aedde50c50-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5eba05b08c884a0bddd0ad96-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5ebff6d34a4cf4438dba5d80-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5eb2200b989caa081980b812-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5dea1d9cec016f062ce8aab1-medium.jpg30.2%
 
https://contestimg.wish.com/api/webimage/5eb4dd169263020a42be1a88-medium.jpg30.2%
 
Other values (1331)154398.1%
 
ValueCountFrequency (%) 
https1573100.0%
 
ValueCountFrequency (%) 
contestimg.wish.com1573100.0%
 
ValueCountFrequency (%) 
/api/webimage/5eba5b1c29367c77b5c0eb35-medium.jpg30.2%
 
/api/webimage/5eb4dd169263020a42be1a88-medium.jpg30.2%
 
/api/webimage/5eba05b08c884a0bddd0ad96-medium.jpg30.2%
 
/api/webimage/5cde56ea6bbbd86b1cbab4a8-medium.jpg30.2%
 
/api/webimage/5cedf93ac0baab7389f4ccd7-medium.jpg30.2%
 
/api/webimage/5eb2200b989caa081980b812-medium.jpg30.2%
 
/api/webimage/5e142dee04c3e579e89576a3-medium.jpg30.2%
 
/api/webimage/5ea91e4d29b81241e1d43b27-medium.jpg30.2%
 
/api/webimage/5eb4f323b67a8d189a8f8380-medium.jpg30.2%
 
/api/webimage/5e9a74e447f7d92c8db8d14b-medium.jpg30.2%
 
Other values (1331)154398.1%
 
ValueCountFrequency (%) 
1573100.0%
 
ValueCountFrequency (%) 
1573100.0%
 

product_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
5cde56ea6bbbd86b1cbab4a8
 
3
5cedf93ac0baab7389f4ccd7
 
3
5eb4f323b67a8d189a8f8380
 
3
5ebff6d34a4cf4438dba5d80
 
3
5ee8875404718a4bba2d6348
 
3
Other values (1336)
1558 
ValueCountFrequency (%) 
5cde56ea6bbbd86b1cbab4a830.2%
 
5cedf93ac0baab7389f4ccd730.2%
 
5eb4f323b67a8d189a8f838030.2%
 
5ebff6d34a4cf4438dba5d8030.2%
 
5ee8875404718a4bba2d634830.2%
 
5ec1e63f7abee20ab93c68f230.2%
 
5ebe0ead593b960eb1c82d0b30.2%
 
5c80e8a150c63d28c67b8f1430.2%
 
5eaa6d9c8d99eb3ec06709f430.2%
 
5ea91e4d29b81241e1d43b2730.2%
 
Other values (1331)154398.1%
 
2020-09-10T22:59:13.097946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1130 ?
Unique (%)71.8%
2020-09-10T22:59:13.223608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length24
Mean length24
Min length24

theme
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
summer
1573 
ValueCountFrequency (%) 
summer1573100.0%
 
2020-09-10T22:59:13.320314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-10T22:59:13.381189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:13.449005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

crawl_month
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2020-08
1573 
ValueCountFrequency (%) 
2020-081573100.0%
 
2020-09-10T22:59:13.544750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-10T22:59:13.607583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:13.673406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Interactions

2020-09-10T22:58:30.431660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:30.587276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:30.693004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:30.800705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:30.915399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.020121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.134821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.241526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.355224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.471913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.581626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.689291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.796043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:31.907750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.013468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.122133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.237861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.343578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.450293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.559002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.674698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.780417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.892115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:32.999787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.128443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.257098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.452611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.565273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.675978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.790707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:33.898419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.005134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.121821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.239507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.356193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.472881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.596514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.710212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.835910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:34.942629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.057319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.175004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.285703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.404390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.512105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.625797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.733512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.846171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:35.963856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.080543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.199226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.320937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.449592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.568275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.691945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.811614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:36.941240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.068899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.188579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.310253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.541635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.669292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.787975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:37.907655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.049278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.166963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.284648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.404328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.524044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.625772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.734480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.837207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:38.946912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:39.061605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:40.497763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:41.080204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:41.195900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:41.989771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:42.788597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:42.894314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:43.425931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:43.538630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:43.776953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:43.895635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:44.268637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:44.395335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:44.521000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:44.649655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:44.895996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:45.017663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:45.281928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:45.408625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:45.533293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:45.771616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:46.020987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:46.139671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:47.001365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:47.365400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:47.499037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:47.607747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:47.716450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:47.825159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:47.939852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.047564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.160262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.265980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.548222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.668907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.778609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.888315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:48.997025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.111680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.219392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.328101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.444789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.552501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:49.674177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:49.929494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:50.052166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:50.303525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:50.432187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:50.685508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:50.810139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:50.929853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:51.044510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:51.169178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-10T22:58:51.550158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:51.664886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:51.779581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:51.895234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.002981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.116678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.223393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.354008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.471732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.580437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.690146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.801848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:52.914543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.023253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.131962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.248652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.366335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.482025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.597716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.721385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.836073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:53.957752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.072447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.195123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.320781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.439466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.557152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.671842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.791522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:54.909209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.025900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.149564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.255281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.362991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.481640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.616280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.738952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:55.869601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.204743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.325418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.442106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.559756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.683425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.805099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:56.933755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.049481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.158192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.274881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.380595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.487309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.597976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.714706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.821416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:57.935075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.040828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.158514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.274210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.381917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.491623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.600295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.714030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.822739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:58.930451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.048134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.169771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.289495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.412159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.540816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.660491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.785163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:58:59.904842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.033498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.164149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.287781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.411487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.534157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.663775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.793429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:00.930064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-10T22:59:14.152127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-10T22:59:14.487194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-10T22:59:14.830313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-10T22:59:15.128514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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2020-09-10T22:59:02.783534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:03.283852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-10T22:59:03.629890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

titletitle_origpriceretail_pricecurrency_buyerunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_nameshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerurgency_textorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_picturemerchant_profile_pictureproduct_urlproduct_pictureproduct_idthemecrawl_month
02020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets16.0014EUR10003.765426.08.010.01.09.00000Summer,Fashion,womenunderwearsuit,printedpajamasset,womencasualshort,Women's Fashion,flamingo,loungewearset,Casual,Shirt,casualsleepwear,Shorts,flamingotshirt,Elastic,Vintage,Tops,tshirtandshortsset,Women,Sleepwear,Print,womenpajamasset,womennightwear,Pajamas,womensleepwearsetwhiteM50Livraison standard4034501.0Quantité limitée !CNzgrdejiazgrdejia(568 notes)5684.128521595097d6a26f6e070cb878d10NaNhttps://www.wish.com/c/5e9ae51d43d6a96e303acdb0https://contestimg.wish.com/api/webimage/5e9ae51d43d6a96e303acdb0-medium.jpg5e9ae51d43d6a96e303acdb0summer2020-08
1SSHOUSE Summer Casual Sleeveless Soirée Party Soirée sans manches Vêtements de plage sexy Mini robe femme wshC1612242400387A21Women's Casual Summer Sleeveless Sexy Mini Dress8.0022EUR2000013.4561352269.01027.01118.0644.01077.00000Mini,womens dresses,Summer,Patchwork,fashion dress,Dress,Mini dress,Women's Fashion,Women S Clothing,backless,party,summer dresses,sleeveless,sexy,CasualgreenXS50Livraison standard2041501.0Quantité limitée !CNSaraHousesarahouse83 % avis positifs (17,752 notes)177523.89967356458aa03a698c35c90509880NaNhttps://www.wish.com/c/58940d436a0d3d5da4e95a38https://contestimg.wish.com/api/webimage/58940d436a0d3d5da4e95a38-medium.jpg58940d436a0d3d5da4e95a38summer2020-08
22020 Nouvelle Arrivée Femmes Printemps et Été Plage Porter Longue Mince Cardigan Ouvert Avant Kimono Vert Feuille Imprimé En Mousseline de Soie Cardigan S-5XL2020 New Arrival Women Spring and Summer Beach Wear Long Thin Cardigan Open Front Kimono Green Leaf Printed Chiffon Cardigan S-5XL8.0043EUR10003.57145.04.02.00.03.00000Summer,cardigan,women beachwear,chiffon,Sexy women,Coat,summercardigan,openfront,short sleeves,Swimsuit,Women's Fashion,leaf,Green,printed,Spring,longcardigan,Women,Beach,kimonoleopardprintXS1Livraison standard3036501.0Quantité limitée !CNhxt520hxt52086 % avis positifs (295 notes)2953.9898315d464a1ffdf7bc44ee933c650NaNhttps://www.wish.com/c/5ea10e2c617580260d55310ahttps://contestimg.wish.com/api/webimage/5ea10e2c617580260d55310a-medium.jpg5ea10e2c617580260d55310asummer2020-08
3Hot Summer Cool T-shirt pour les femmes Mode Tops Abeille Lettres imprimées Manches courtes O Neck Coton T-shirts Tops Tee VêtementsHot Summer Cool T Shirt for Women Fashion Tops Bee Printed Letters Short Sleeve O Neck Cotton T-shirts Tops Tee Clothing8.008EUR500014.03579295.0119.087.042.036.00000Summer,Shorts,Cotton,Cotton T Shirt,Sleeve,printedletterstop,Clothing,Tops,Necks,short sleeves,Women's Fashion,Women Clothing,printed,Women,tshirtforwomen,Fashion,T Shirts,ShirtblackM50Livraison standard204150NaNNaNCNallenfanallenfan(23,832 notes)238324.02043558cfdefdacb37b556efdff7c0NaNhttps://www.wish.com/c/5cedf17ad1d44c52c59e4acahttps://contestimg.wish.com/api/webimage/5cedf17ad1d44c52c59e4aca-medium.jpg5cedf17ad1d44c52c59e4acasummer2020-08
4Femmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XLWomen Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL2.723EUR10013.10206.04.02.02.06.00000Summer,Plus Size,Lace,Casual pants,Bottom,pants,Loose,Women's Fashion,Shorts,Lace Up,Elastic,Casual,WomenyellowS1Livraison standard1035501.0Quantité limitée !CNyoungpeopleshophappyhorses85 % avis positifs (14,482 notes)144824.0015885ab3b592c3911a095ad5dadb0NaNhttps://www.wish.com/c/5ebf5819ebac372b070b0e70https://contestimg.wish.com/api/webimage/5ebf5819ebac372b070b0e70-medium.jpg5ebf5819ebac372b070b0e70summer2020-08
5Plus la taille d'été femmes décontracté sans manches barboteuses combinaisons combinaison de couleur unie jarretelles pantalons lâche salopettePlus Size Summer Women Casual Sleeveless Rompers Jumpsuits Solid Color Suspender Ttrousers Loose Overalls3.929EUR1005.0011.00.00.00.00.00000Deep V-Neck,Summer,Plus Size,Spaghetti Strap,Overalls,Women's Fashion,sleeveless,Women,Casual,jumpsuitnavyblueSize-XS1Livraison standard104050NaNNaNCNzhoulinglingazhoulinglinga75 % avis positifs (65 notes)653.5076925e4b9c3801ba9d210036fc5a0NaNhttps://www.wish.com/c/5ec645bafd107a02279c8c54https://contestimg.wish.com/api/webimage/5ec645bafd107a02279c8c54-medium.jpg5ec645bafd107a02279c8c54summer2020-08
6Women Fashion Loose Lace Blouse Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Grande Taille XS-8XLWomen Fashion Loose Lace Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Size XS-8XL7.006EUR5000003.8467423172.01352.0971.0490.0757.00000blouse,Women,lace t shirt,summer t-shirts,Lace,Sleeve,Women Blouse,loose shirt,Short Sleeve Blouses,Pure Color,Womens Blouse,Bat,lace shirts,Necks,Women's Fashion,Plus Size,loose t-shirt,Short Sleeve T-Shirt,Fashion,Tops,ShirtwhiteXS50Livraison standard203150NaNNaNCNUnique Li Fashion Shopuniquelifashionshopbb657bfe91d211e598c7063a14dc88b586 % avis positifs (10,194 notes)101944.0765165652f4053a698c76dc9a3f371https://s3-us-west-1.amazonaws.com/sweeper-production-merchantimage/dp_5652f4053a698c76dc9a3f37.jpghttps://www.wish.com/c/5c63a337d5e2ce4bbb3152cfhttps://contestimg.wish.com/api/webimage/5c63a337d5e2ce4bbb3152cf-medium.jpg5c63a337d5e2ce4bbb3152cfsummer2020-08
7Robe tunique ample femme Robe d'été Robe en jean Robe chemise en jean Robe droiteWomen's Baggy Tunic Dress Summer Dress Denim Dress Denim Shirt Dress Shift Dress12.0011EUR100003.76286120.056.061.018.031.00000Jeans,Fashion,tunic,Shirt,Summer,Dress,Denim,summer dress,denimjeansdres,short sleeves,casual dresses,Women's Fashion,Tunic dress,minishirtdres,Lines,mididreblueM.50Livraison standard3013950NaNNaNCNSo Bandsoband(342 notes)3423.6812875d45349676befe65691dcfbb0NaNhttps://www.wish.com/c/5e0ae5ebc2efb76ccf0a3391https://contestimg.wish.com/api/webimage/5e0ae5ebc2efb76ccf0a3391-medium.jpg5e0ae5ebc2efb76ccf0a3391summer2020-08
8Robe d'été décontractée à manches courtes pour femmesWomen's Summer Casual Dress Fashion Short Sleeve Slim Dress11.0084EUR10013.47156.02.03.01.03.00000slim dress,summer dress,womenshortsleevedre,Sleeve,Summer,Dress,slim,short sleeves,Women's Fashion,Shorts,boho dress,slimfitdre,Fashion,CasualblackM50Livraison standard2036501.0Quantité limitée !CNchenxiangjunjunchenxiangjunjun82 % avis positifs (330 notes)3303.8030305d42980e8388970d32294ddc0NaNhttps://www.wish.com/c/5e6f1fb7fe4a5bb4b8bf36e5https://contestimg.wish.com/api/webimage/5e6f1fb7fe4a5bb4b8bf36e5-medium.jpg5e6f1fb7fe4a5bb4b8bf36e5summer2020-08
9Femmes d'été, plus la taille décontractée lâche col en V à manches courtes imprimé floral Blouse TopsSummer Women Plus Size Casual Loose V Neck Short Sleeve Floral Printed Blouse Tops5.7822EUR500003.60687287.0128.092.068.0112.00000blouse,Summer,Plus Size,Floral print,Necks,Sleeve,summer shirt,Loose,short sleeves,Casual,T Shirts,Shorts,Fashion,Floral,Women,Women's Fashion,Tops,printedbeigeS50Livraison standard203350NaNNaNCNLuowei clotheluoweiclothe85 % avis positifs (5,534 notes)55343.9998195ba2251b4315d12ebce873fa0NaNhttps://www.wish.com/c/5ccfaf238a8d535cec2dfb47https://contestimg.wish.com/api/webimage/5ccfaf238a8d535cec2dfb47-medium.jpg5ccfaf238a8d535cec2dfb47summer2020-08

Last rows

titletitle_origpriceretail_pricecurrency_buyerunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_nameshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerurgency_textorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_picturemerchant_profile_pictureproduct_urlproduct_pictureproduct_idthemecrawl_month
1563ZANZEA Femmes Été Polka Dot Kaftan Beach Club Party Longue Maxi Dress HOT Long DressZANZEA Women Summer Polka Dot Kaftan Beach Club Party Long Maxi Dress HOT Long Dress15.0092EUR10003.517426.015.015.07.011.00000Summer,loosedresse,kaftan,long dress,baggydres,Dress,Polkas,robesforwomen,Women's Fashion,34sleevedres,party,roundneckdres,vestido,maxi dress,Beach,polka dot,WomenredL50Envio Padrão403850NaNNaNCNfashionforgirlsguangzhouchanny88% Feedback positivo (151,914 classificações)1519144.12792153aa664438d3046ee44a50240NaNhttps://www.wish.com/c/5da04c1f5949a226113006f1https://contestimg.wish.com/api/webimage/5da04c1f5949a226113006f1-medium.jpg5da04c1f5949a226113006f1summer2020-08
15642018 Femme mode d'été en dentelle Patchwork Patchwork Débardeurs Débardeurs Casual Sleeveless Tops Gilet Chemisier (S-5XL) Grande taille2018 Women fashion Summer Lace Patchwork Tank Tops Casual Sleeveless Tops Vest Blouse (S-5XL) Plus Size5.915EUR100003.38414156.058.072.045.083.00000blouse,Summer,Vest,Fashion,Women Blouse,sleevelessblouse,summer shirt,Loose,tank top,Casual,summerblouse,Women's Fashion,sleeveless tops,women top,casualblouse,WomenWhiteS50Livraison standard2036501.0Quantité limitée !CNliminnyliminny81 % avis positifs (12,134 notes)121343.86690358aec90823ef726994a323fe0NaNhttps://www.wish.com/c/5b4ed29514f0765a8a844592https://contestimg.wish.com/api/webimage/5b4ed29514f0765a8a844592-medium.jpg5b4ed29514f0765a8a844592summer2020-08
1565Nouveau Pantalon De Mode D'été Femmes Leggings Pantalon Déchiré Pantalon Mince Armée Vert Collants PantalonNew Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants3.008EUR10013.795725.010.013.03.06.00000Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,WomenskyblueXS1Livraison standard1041501.0Quantité limitée !CNbujizhanbujizhan(4,080 notes)40803.987990584a7a381591451e4e3af3df0NaNhttps://www.wish.com/c/5e8f0165e815903d022a3c7chttps://contestimg.wish.com/api/webimage/5e8f0165e815903d022a3c7c-medium.jpg5e8f0165e815903d022a3c7csummer2020-08
1566Robe mi-longue d'été à manches courtes pour femmes Baggy Robes pour femmes Shift Kaftan S-5XLWomens Short Sleeve Baggy Summer Beach Midi Dress Ladies Shift Kaftan Dresses S-5XL11.00134EUR10013.54287.011.04.02.04.00000Summer,Shift Dress,Sleeve,shirt dress,long dress,Beach,Dress,short sleeves,beach dress,Shorts,Midi Dress,Ladies,Women's Fashion,loose dress,kaftandreblackS50Livraison standard304650NaNNaNCNSCOMELYscomely86 % avis positifs (1,926 notes)19264.071651593402ae25c4f54ed4e0abdf0NaNhttps://www.wish.com/c/5d1060d39ed281190dfcec91https://contestimg.wish.com/api/webimage/5d1060d39ed281190dfcec91-medium.jpg5d1060d39ed281190dfcec91summer2020-08
1567Combinaison sans manches pour femmes couleur unie Dames Slim Short Bodycon Rompers Femmes BodySleeveless Solid Color Women Jumpsuit Ladies Slim Short Bodycon Rompers Women Bodysuit8.007EUR2000014.2531271919.0580.0304.0128.0196.01010bodycon jumpsuits,nightwear,Shorts,slim,Body Suit,shortjumpsuit,Women,vestido,Ladies,sleeveless,sexy,Rompers,Casual,jumpsuitblackM50Livraison standard204450NaNNaNCNRell Mailrellmail88 % avis positifs (16,803 notes)168034.15503256455b13b15aab129db58cb70NaNhttps://www.wish.com/c/5c91a7ae7cfe8e4e64c36d97https://contestimg.wish.com/api/webimage/5c91a7ae7cfe8e4e64c36d97-medium.jpg5c91a7ae7cfe8e4e64c36d97summer2020-08
1568Nouvelle Mode Femmes Bohême Pissenlit Imprimer Tee Shirt Lady Fille T-shirt À Manches Courtes Boho Graphique Tee Casual Yoga Top Plus La TailleNew Fashion Women Bohemia Dandelion Print Tee Shirt Lady Girl Short Sleeve T-shirt Boho Graphic Tee Casual Yoga Top Plus Size6.009EUR1000014.081367722.0293.0185.077.090.00000bohemia,Plus Size,dandelionfloralprinted,short sleeves,yoga top,bohotshirt,Cool T-Shirts,Women's Fashion,Fashion,short sleeve shirt,Casual,Women,Shorts,Yoga,Shirt,Sleeve,graphic tee,Tee Shirt,T Shirts,boho,bohoshirt,Print,Casual Tops,TopsnavyblueS50Livraison standard204150NaNNaNCNcxuelin99126cxuelin9912690 % avis positifs (5,316 notes)53164.2246055b507899ab577736508a07820NaNhttps://www.wish.com/c/5d5fadc99febd9356cbc52eehttps://contestimg.wish.com/api/webimage/5d5fadc99febd9356cbc52ee-medium.jpg5d5fadc99febd9356cbc52eesummer2020-08
156910 couleurs femmes shorts d'été lacent ceinture élastique culotte lâche, plus la taille S-6XL10 Color Women Summer Shorts Lace Up Elastic Waistband Loose Panties Plus Size S-6XL2.0056EUR10013.072811.03.01.03.010.00000Summer,Panties,Elastic,Lace,Casual pants,casualshort,summer shorts,Plus Size,Short pants,women shorts,Shorts,Beach Shorts Women,Beach Shorts,loosepant,high waisted shorts,Lace Up,Women's Fashion,WomenlightblueS2Livraison standard1026501.0Quantité limitée !CNsell best quality goodssellbestqualitygoods(4,435 notes)44353.69605454d83b6b6b8a771e478558de0NaNhttps://www.wish.com/c/5eccd22b4497b86fd48f16b4https://contestimg.wish.com/api/webimage/5eccd22b4497b86fd48f16b4-medium.jpg5eccd22b4497b86fd48f16b4summer2020-08
1570Nouveautés Hommes Siwmwear Beach-Shorts Hommes Summer Short de bain court à séchage rapide Beach-Wear SportsNew Men Siwmwear Beach-Shorts Men Summer Quick-Dry Short Swim-Shorts Beach-Wear Sports5.0019EUR10003.715924.015.08.03.09.00000runningshort,Beach Shorts,beachpant,menbeachshort,Men,sailboatshort,beach swimwear,Men's Fashion,Shorts,Summer,men's shorts,SportwhiteSIZE S15Livraison standard201150NaNNaNCNshixueyingshixueying86 % avis positifs (210 notes)2103.9619055b42da1bf64320209fc8da690NaNhttps://www.wish.com/c/5e74be96034d613d42b52dfehttps://contestimg.wish.com/api/webimage/5e74be96034d613d42b52dfe-medium.jpg5e74be96034d613d42b52dfesummer2020-08
1571Mode femmes d'été sans manches robes col en V dos nu robe en dentelle dames robes de plage robe blancheFashion Women Summer Sleeveless Dresses V Neck Backless Lace Dress Ladies Beach Dresses White Dress13.0011EUR10002.5020.01.00.00.01.00000Summer,fashion women,Fashion,Lace,Dresses,Dress,Lace Dress,Women's Fashion,ladies dress,beach dress,Sleeveless dress,backless,women's dress,sleeveless,Ladies,women dress,V-neck Dresses,Women,Beach,white,NeckswhiteSize S.36Livraison standard302950NaNNaNCNmodaimodai77 % avis positifs (31 notes)313.7741945d56b32c40defd78043d5af90NaNhttps://www.wish.com/c/5eda07ab0e295c2097c36590https://contestimg.wish.com/api/webimage/5eda07ab0e295c2097c36590-medium.jpg5eda07ab0e295c2097c36590summer2020-08
1572Pantalon de yoga pour femmes à la mode Slim Fit Fitness Running LeggingsFashion Women Yoga Pants Slim Fit Fitness Running Leggings7.006EUR10014.07148.03.01.00.02.00000Summer,Leggings,slim,Yoga,pants,Slim Fit,Women's Fashion,Running,Fashion,Sport,Fitness,WomenredS50Livraison standard204150NaNNaNCNAISHOPPINGMALLaishoppingmall90 % avis positifs (7,023 notes)70234.2359395a409cf87b584e7951b2e25f0NaNhttps://www.wish.com/c/5e857321f53c3d2d8f25e7edhttps://contestimg.wish.com/api/webimage/5e857321f53c3d2d8f25e7ed-medium.jpg5e857321f53c3d2d8f25e7edsummer2020-08